FWIW on a 5080 16GB it takes 3 minutes for 10 seconds 480p video (the mouse video workflow with length changed from 5 seconds to 10 seconds)

▲ Sohcahtoa82 17 minutes ago | parent | next [-]

I must be doing something incredibly wrong.

I'm on an RTX 5090. I told it to make a 5 second 864x480 video, it's been running for over 30 minutes and is only 35% done in the SamplerCustomAdvanced step.

EDIT: Oh, I'm an idiot. Forgot I had a llama.cpp webserver running with a model loaded. Killed it and it finished very fast.

▲ embedding-shape 6 hours ago | parent | prev [-]

FWIW on a 6000 Pro it takes 68 seconds for 10 seconds 480p video, (cold) same demo workflow as you used. Set "megapixels" to 2.0 (1920x1088) and same video seems to take 5+ minutes, not sure if everything is right/correct at the moment.

As far as I can tell, the current ComfyUI nodes don't even do compilation, and I haven't looked into what attention mechanism they're using, but I'm sure with time these durations will come down even more.

▲ pkroll 5 hours ago | parent | next [-]

People on reddit have definitely pointed out that sageattention will speed up the renders.

And it's literally the first day. Someone will make a distilled 4-8 step LoRA and we're off to the races.

Edit: did a couple of 10 second long 864/480 i2v videos on my RTX Pro 6000: sageattention bumps them up 33%, that is to say, 140.89 seconds without sageattention becomes 105.69 with sageattention on (if using the KJ Sageattention node, "allow_compile" doesn't seem to affect it, just "sage_attention" set to "auto" works fine).

EasyCache also appears to work, but does affect quality, at least with the default threshold or even down to 0.10. Still, at 0.10 threshold the same render above, with sageattention, is down to 71.33 seconds, so depending on your use case the quality hit might be worth it. Also it seems that with EasyCache the video still matches the un-EasyCached video (with the same seed), so you could use it to do seed hunting.

	▲	loldog 16 minutes ago \| parent [-]
		Knowing r/StableDiffusion this will run on a Raspberry Pico's PIO core in real-time by end of this week.

▲ wincy 2 hours ago | parent | prev | next [-]

What’s the vram usage? I’ve got a 5090 so as long as I can fit it in memory the gen times should be roughly equivalent ~10%

	▲	embedding-shape 21 minutes ago \| parent [-]
		Depends on what weights, I landed on: `diffusion model: minimax_h3_fl2va_bf16.safetensors text encoder: qwen3vl_32b_minimax_h3_nvfp4_awq.safetensors video VAE: minimax_h3_video_vae_fp16.safetensors audio VAE: minimax_h3_audio_vae_fp32.safetensors` Ends up at ~83GB, but they also shipped bunch of pruned + quantized versions of the diffusion model, might fit with a 5090: https://huggingface.co/Comfy-Org/MiniMax-H3

▲ teaearlgraycold 2 hours ago | parent | prev [-]

Waiting for the “FWIW on a B200” reply

	▲	embedding-shape 28 minutes ago \| parent \| next [-]
		I was kind of hoping to induce that too! But sadly, no one has come to our rescue yet.
	▲	halJordan an hour ago \| parent \| prev [-]
		Runpod waiting for you